Downhole Real-Time Filtrate Contamination Monitoring

ABSTRACT

A method includes identifying linearly behaving data within obtained data associated with fluid obtained from a subterranean formation. Shrinkage factor is determined based on the linearly behaving data. A function relating GOR data of the obtained fluid with the determined shrinkage factor is determined. A first linear relationship between optical density (OD) data of the obtained fluid and the function is determined. A second linear relationship between density data of the obtained fluid and the function is determined. An oil-based mud (OBM) filtrate contamination property of OBM filtrate within the obtained fluid based on the first linear relationship is determined. A native formation property of native formation fluid within the obtained fluid based on the second linear relationship is determined. A volume fraction of OBM filtrate contamination within the obtained fluid based on the OBM filtrate contamination property and the native formation property is estimated.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is the Divisional of earlier filed U.S. Non-Provisionalapplication Ser. No. 14/697,382, filed Apr. 27, 2015 which claimspriority to and the benefit of U.S. Provisional Application No.61/985,376, entitled “Downhole Real-Time Filtrate ContaminationMonitoring,” filed Apr. 28, 2014, and also claims priority to and thebenefit of U.S. Provisional Application No. 62/108,937, entitled“Formation Volume Factor and API Gravity Log Downhole in Real-Time,”filed Jan. 28, 2015, the entire disclosures of which are herebyincorporated herein by reference.

BACKGROUND OF THE DISCLOSURE

The present disclosure pertains to downhole oil-based mud (OBM) filtratecontamination monitoring (OCM) in real time. A known OCM approachutilizes an optical density mixing rule in which both optical densityendpoints of a pure OBM filtrate and a pure native formation fluid areknown. However, existing OCM is limited with respect to high gas-oilratio (GOR) fluids, such as volatile oils and gas condensates, and forformation fluids with little to no optical density contrast relative toOBM filtrate.

SUMMARY OF THE DISCLOSURE

This summary is provided to introduce a selection of concepts that arefurther described below in the detailed description. This summary is notintended to identify indispensable features of the claimed subjectmatter, nor is it intended for use as an aid in limiting the scope ofthe claimed subject matter.

The present disclosure introduces a method that includes identifyinglinearly behaving data within obtained data associated with fluidobtained from a subterranean formation. Shrinkage factor is determinedbased on the linearly behaving data. A function relating GOR data of theobtained fluid with the determined shrinkage factor is determined. Afirst linear relationship between optical density (OD) data of theobtained fluid and the function is determined. A second linearrelationship between density data of the obtained fluid and the functionis determined. An oil-based mud (OBM) filtrate contamination property ofOBM filtrate within the obtained fluid based on the first linearrelationship is determined. A native formation property of nativeformation fluid within the obtained fluid based on the second linearrelationship is determined. A volume fraction of OBM filtratecontamination within the obtained fluid based on the OBM filtratecontamination property and the native formation property is estimated.

The present disclosure also introduces a method that includes a stocktank oil (STO) basis density of a fluid obtained from a subterraneanformation based on data associated with the obtained fluid. The dataincludes gas-oil ratio (GOR) data. The estimated STO-basis density ofthe obtained fluid is fitted as a function of the GOR data to determinean STO-basis density of oil-based mud (OBM) filtrate contamination inthe obtained fluid and a parameter relating the STO-basis density of theobtained fluid, the STO-basis density of the OBM filtrate contamination,and the GOR data. The STO-basis density of the obtained fluid isdetermined based on the determined STO-basis density of the OBM filtratecontamination, the parameter, and the GOR data, thus obtaining a log ofthe STO-basis density of the obtained fluid with respect to volume ofthe obtained fluid or time elapsed during obtaining the obtained fluid.An API gravity log is determined based on the log of the STO-basisdensity of the obtained fluid. A volume fraction of OBM filtratecontamination within the obtained fluid based on the API gravity log isestimated.

The present disclosure also introduces a method that includesdetermining a linear relation between formation volume factor (FVF) dataof a fluid obtained from a subterranean formation and gas-oil ratio(GOR) data of the fluid. Data associated with the obtained fluidincludes the FVF data and GOR data. Stock tank oil (STO) basis densityof native formation fluid within the obtained fluid is determined basedon a slope of the linear relation, a GOR of the native formation fluid,and a density of the native formation fluid. A parameter is thendetermined, relating STO-basis density of the native formation fluid,STO-basis density of oil-based mud (OBM) filtrate contamination withinthe obtained fluid, and GOR of the native formation fluid. STO-basisdensity of the obtained fluid is determined based on the STO-basisdensity of OBM filtrate contamination, the parameter, and the GOR data.

These and additional aspects of the present disclosure are set forth inthe description that follows, and/or may be learned by a person havingordinary skill in the art by reading the materials herein and/orpracticing the principles described herein. At least some aspects of thepresent disclosure may be achieved via means recited in the attachedclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is understood from the following detaileddescription when read with the accompanying figures. It is emphasizedthat, in accordance with the standard practice in the industry, variousfeatures are not drawn to scale. In fact, the dimensions of the variousfeatures may be arbitrarily increased or reduced for clarity ofdiscussion.

FIG. 1 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 2 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 3 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 4 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 5 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 6 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 7 is a schematic view of at least a portion of apparatus accordingto one or more aspects of the present disclosure.

FIG. 8 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 9 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 10 is a flow-chart diagram of at least a portion of a methodaccording to one or more aspects of the present disclosure.

FIG. 11 includes four graphs each depicting one or more aspects of thepresent disclosure.

FIG. 12 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 13 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 14 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 15 is a flow-chart diagram of at least a portion of a methodaccording to one or more aspects of the present disclosure.

FIG. 16 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 17 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 18 includes three graphs each depicting one or more aspects of thepresent disclosure.

FIG. 19 includes six graphs each depicting one or more aspects of thepresent disclosure.

FIG. 20 is a flow-chart diagram of at least a portion of a methodaccording to one or more aspects of the present disclosure.

FIG. 21 includes two graphs each depicting one or more aspects of thepresent disclosure.

FIG. 22 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 23 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 24 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 25 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 26 is a flow-chart diagram of at least a portion of a methodaccording to one or more aspects of the present disclosure.

FIG. 27 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 28 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 29 is a graph depicting one or more aspects of the presentdisclosure.

FIG. 30 is a schematic view of at least a portion of apparatus accordingto one or more aspects of the present disclosure.

FIG. 31 is a schematic view of at least a portion of apparatus accordingto one or more aspects of the present disclosure.

FIG. 32 is a schematic view of at least a portion of apparatus accordingto one or more aspects of the present disclosure.

FIG. 33 is a schematic view of at least a portion of apparatus accordingto one or more aspects of the present disclosure.

DETAILED DESCRIPTION

It is to be understood that the following disclosure provides manydifferent embodiments, or examples, for implementing different featuresof various embodiments. Specific examples of components and arrangementsare described below to simplify the present disclosure. These are, ofcourse, merely examples and are not intended to be limiting. Inaddition, the present disclosure may repeat reference numerals and/orletters in the various examples. This repetition is for the purpose ofsimplicity and clarity and does not in itself dictate a relationshipbetween the various embodiments and/or configurations discussed.Moreover, the formation of a first feature over or on a second featurein the description that follows may include embodiments in which thefirst and second features are formed in direct contact, and may alsoinclude embodiments in which additional features may be formedinterposing the first and second features, such that the first andsecond features may not be in direct contact.

OCM may be utilized in conjunction with multiple fluid properties, suchas GOR, density, optical density (OD), and composition, among others.For example, existing methods may fit GOR directly with OBMcontamination utilizing a power function to obtain GOR values for thenative formation fluid and the OBM filtrate contamination. However,while the obtained OBM filtrate contamination is on a stock tank oil(STO) basis, it is often treated equivalent to that on the live fluidbasis. This can result in a large error for high GOR fluids. Becauseformation volume factor (FVF, the reciprocal of the shrinkage factor) isfar away from unity for high GOR fluids, FVF (or shrinkage factor)varies from approximately one (corresponding to the pure OBM filtrate)to a large number during cleanup. This is exhibited in the examplelaboratory data shown in FIG. 1, which depicts the linear variation 5 ofFVF (1/b) relative to GOR of a known gas condensate during a simulatedcleanup process, where GOR=0 corresponds to pure OBM filtrate, andGOR=GOR₀ corresponds to native formation fluid. The large change in FVFduring cleanup renders a linear relationship between GOR and density(and/or OD) invalid for high GOR fluids over an entire OBM filtratecontamination range.

For example, FIG. 2 illustrates GOR variations as a function of OBMvolume percentage for an example black oil, and FIG. 3 illustrates GORvariations as a function of OBM volume percentage for an example gascondensate. In FIG. 2, STO based data 10 for the example black oilreflects a linear relationship 15 between GOR and OBM volume percentagefor the entire contamination range (0-100%), while live fluid based data20 for the example black oil reflects a non-linear relationship 25between GOR and OBM volume percentage. Similarly, in FIG. 3, STO baseddata 30 for the example gas condensate reflects a linear relationship 35between GOR and OBM volume percentage for the entire contamination range(0-100%), while live fluid based data 40 for the example gas condensatereflects a non-linear relationship 45 between GOR and OBM volumepercentage. FIGS. 2 and 3 demonstrate that large errors may occur if alinear relationship between GOR and other fluid properties is utilizedto extrapolate those other properties to native formation fluid and pureOBM filtrate, when GOR is not corrected for shrinkage.

Such errors are further demonstrated in FIG. 4, which depicts arelationship 50 between density and GOR for an example gas condensate.That is, one can linearize the early time cleanup data (in the low GORand high OBM filtrate contamination range) and show that anextrapolation 52 to GOR₀ (native formation fluid) results in a largeerror. In FIG. 4, the extrapolation 52 to GOR₀ indicates a GOR₀ of about3350 scf/bbl, substantially less than the true GOR that is greater than10,000 scf/bbl. Similarly, when linearizing using late time cleanup data(in the high GOR and low OBM filtrate contamination range), anextrapolation 54 to zero GOR (pure OBM filtrate) also leads to a largeerror. In FIG. 4, the extrapolation 54 to zero GOR indicates a densityof about 0.58 g/cc, substantially less than the true density of about0.88 g/cc.

However, as also shown in FIG. 4, a linear relationship 56 can beobtained between density and an auxiliary function ƒ=[GOR₀−(GOR₀−GOR)b],referred to as the ƒ function. Further details about the ƒ function aredescribed below.

It is also noted that existing OD multi-channel OCM processing mandatesthat measured fluids have sufficient OD contrast in the referencechannel. However, if the OD contrast in the reference channel is notsufficient, the processing is inaccurate. One or more aspects of thepresent disclosure, however, pertain to methods that determineproperties of the native formation fluid and OBM filtrate for low andhigh GOR fluids (and other kinds of formation fluids).

Live fluid based OBM filtrate contamination in volume fraction (ν_(obm))can be expressed in different ways according to different sensors. Forexample, Equation (1) set forth below (derived from the Beer-Lambertlaw) is applicable to optical sensors.

$\begin{matrix}{v_{obm} = \frac{{OD}_{0\; i} - {OD}_{i}}{{OD}_{0\; i} - {OD}_{obmi}}} & (1)\end{matrix}$

where, for channel i, OD_(0i) is the optical density of the nativeformation fluid, OD_(i) is the optical density of the contaminated fluidobtained by a downhole fluid analysis (DFA) tool (referred to asapparent optical density), and OD_(obmi) is the optical density of theOBM filtrate.

Similarly, Equation (2) set forth below is applicable to densitysensors.

$\begin{matrix}{v_{obm} = \frac{\rho_{0} - \rho}{\rho_{0} - \rho_{obm}}} & (2)\end{matrix}$

where ρ₀ is the density of the native formation fluid, ρ is the densityof the contaminated fluid obtained by the DFA tool (referred to asapparent density), and ρ_(obm) is the density of the pure OBM filtrate.

Similarly, Equation (3) set forth below is applicable to GOR.

$\begin{matrix}{{v_{obm} = {{bv}_{obmSTO} = {b\frac{{GOR}_{0} - {GOR}}{{GOR}_{0}}}}},{b = \frac{B_{OBM}}{B_{o}}}} & (3)\end{matrix}$

where GOR₀ is the gas/oil ratio of the native formation fluid and GOR isthe gas/oil ratio of the contaminated fluid obtained by the DFA tool(referred to as apparent GOR).

Apparent GOR can be obtained from DFA measurements at a series of timesduring cleanup. The OBM filtrate contamination level in volume fractionbased on stock tank oil (ν_(obmSTO)) can be converted to that based onthe live fluid at downhole conditions by the shrinkage factor b.

The formation volume factor (B_(o)) of the formation fluid is defined asthe ratio of the volume (V) of the formation fluid at formationconditions to the volume of stock tank oil at standard conditions(V_(STOStd)), as set forth below in Equation (4).

$\begin{matrix}{B_{o} = {\frac{V}{V_{STOStd}} = {{\left( \frac{\rho_{STOStd}}{\rho} \right)\left( {1 + {\frac{GOR}{\rho_{STOStd}}\frac{M_{gas}P_{Std}}{{RT}_{Std}}}} \right)} = {\left( \frac{\rho_{STOStd}}{\rho} \right)\left( {1 + \frac{{GORM}_{gas}}{23.69\mspace{14mu} \rho_{STOStd}}} \right)}}}} & (4)\end{matrix}$

where ρ_(STOStd) is the density of STO at standard conditions andM_(gas) is the gas molecular weight at standard conditions. P_(Std) andT_(Std) are the pressure and temperature of standard conditions (14.7psi and 60 degrees F.), and R is the gas constant, which is 23.69 basedon SI units.

Apparent GOR and density may be measured by DFA and/or obtained fromsuch measurements. The gas molecular weight M_(gas) may be calculatedaccording to composition data, which may also be determined via DFA.

An artificial neural network (ANN) that is utilized in existing DFA GORalgorithms may be utilized to estimate ρ_(STOStd). However, ifρ_(STOStd) determined via ANN processing is not sufficiently accurate,one can assume that ρ_(STOStd) is approximately constant if densities ofthe OBM filtrate and formation fluid at standard conditions aresufficiently similar. OBM filtrate contamination may also or instead bebased on STO, such as via utilization of Equation (5) set forth below.

$\begin{matrix}{v_{obmSTO} = {\frac{\rho_{0{STOStd}} - \rho_{STOStd}}{\rho_{0{STOStd}} - \rho_{obmStd}} = \frac{\left( {{GOR}_{0} - {GOR}} \right)}{{GOR}_{0}}}} & (5)\end{matrix}$

Accordingly, ρ_(STOStd) is linearly related to GOR. This may be utilizedas set forth below in Equation (6).

$\begin{matrix}{\rho_{STOStd} = {\rho_{obmStd} - {\frac{\rho_{0{STO}} - \rho_{obmStd}}{{GOR}_{0}}{GOR}}}} & (6)\end{matrix}$

Equation (6) utilizes OBM filtrate density at standard conditions. Onecan assume that the density of OBM filtrate at standard conditions(ρ_(obmStd)) is approximately equal to that at flowline conditions(ρ_(obm)) if an extrapolation method is utilized to obtain ρ_(obm). Onecan also calculate ρ_(obmStd) and ρ_(obm) utilizing correlationsaccording to different types of OBM, such as may include diesel, mineraloils, and/or synthetic-based muds (e.g., n-paraffins, olefins, and/oresters). The densities of these OBMs may be measured by a PVT laboratoryand/or obtained from publicly available literature. The ranges oftemperatures and pressures may cover various formation and standardconditions. The experimental density measurements may then be correlatedby the polynomial function of temperature (degrees F.) and pressure(psia) set forth below in Equation (7).

$\begin{matrix}{\rho_{obm} = {\sum\limits_{i = 0}^{2}{\sum\limits_{j = 0}^{1}{a_{ij}P^{i}T^{j}}}}} & (7)\end{matrix}$

where each a_(ij) is a coefficient of the polynomial function, regressedby matching the experimental density data for different OBM. Theρ_(obmStd) may also be obtained utilizing a recent DFA station, orprevious wells if the same type of OBM is utilized. The ρ_(STOStd) maythen be populated according to GOR utilizing Equation (6) if GOR₀ andρ_(0STOStd) or ρ_(STOStd) are known for a corresponding GOR.

The formation volume factor of the OBM filtrate B_(oobm) is defined asthe ratio of the volume of the OBM filtrate at formation conditionsV_(obm) to the volume at standard conditions V_(obmStd), as set forthbelow in Equation (8).

$\begin{matrix}{B_{oobm} = {\frac{V_{obm}}{V_{obmStd}} = \frac{\rho_{obmStd}}{\rho_{obm}}}} & (8)\end{matrix}$

B_(oobm) is approximately equal to unity, resulting in Equation (9) setforth below.

$\begin{matrix}{\frac{1}{b} = {\left( \frac{\rho_{obm}}{\rho_{obmStd}} \right)\left( \frac{\rho_{STOStd}}{\rho} \right)\left( {1 + \frac{{GORM}_{gas}}{23.69\rho_{STOStd}}} \right)}} & (9)\end{matrix}$

It is noted that when GOR=0 (pure OBM filtrate), ρ_(STOStd)=ρ_(obmStd)and ρ=ρ_(obm). Thus, b=1.

Also, Equation (10), set forth below, applies when GOR=GOR₀.

$\begin{matrix}{\frac{1}{b_{0}} = {\left( \frac{\rho_{obmStd}}{\rho_{obm}} \right)\left( \frac{\rho_{0{Std}}}{\rho_{0}} \right)\left( {1 + \frac{{GOR}_{0}M_{gas}}{23.69\rho_{0}}} \right)}} & (10)\end{matrix}$

Once GOR₀ is obtained, b₀ may be determined utilizing Equation (10).

The formation volume factor and/or shrinkage factor may also bedetermined in other ways within the scope of the present disclosure.

OBM filtrate contamination in volume fraction based on live fluid may beexpressed as set forth below in Equation (11).

$\begin{matrix}{v_{obm} = {\frac{{OD}_{0i} - {OD}_{i}}{{OD}_{0i} - {OD}_{obmi}} = {\frac{\rho_{0} - \rho}{\rho_{0} - \rho_{obm}} = \frac{\left( {{GOR}_{0} - {GOR}} \right)b}{\left( {GOR}_{0} \right)}}}} & (11)\end{matrix}$

For the given formation fluid and OBM filtrate, properties of the nativeformation fluid and pure OBM filtrate are constant, including OD_(0i),OD_(obmi), ρ₀, ρ_(obm), and GOR₀. Therefore, from Equation (11), therelationships among OD_(i), ρ, and (GOR₀−GOR)b are linear. Thus, one mayconsider the auxiliary function g set forth below in Equation (12),which is referred to as the g function.

g=(GOR₀−GOR)b  (12)

When g=0, GOR=GOR₀. Thus, the g function may be fit utilizing a powerfunction instead of GOR itself, which may provide more consistentresults when GOR is obtained from data from multiple sensors.

If a power function is utilized to fit the cleanup data, such as maycomprise an array of OD, density, and GOR relative to pumped volume orpumping time, one may utilize Equation (13) set forth below.

$\begin{matrix}{v_{obm} = {\frac{{OD}_{0i} - {OD}_{i}}{{OD}_{0i} - {OD}_{obmi}} = {\frac{\rho_{0} - \rho}{\rho_{0} - \rho_{obm}} = {\frac{g}{{GOR}_{0}} = {\beta \; V^{- \gamma}}}}}} & (13)\end{matrix}$

where V is the pumped volume, which can be replaced by pumping time t,and β and γ are adjustable fitting parameters. When pumped volume orpumping time approaches infinity, ν_(obm) approaches zero, correspondingto the pure native formation fluid.

Rearrangement of the above relations results in Equations (14), (15),(16), and (16A), set forth below.

OD_(0i)−OD_(i)=(OD_(0i)−OD_(obmi))βV ^(−γ)=β_(1i) V ^(−γ)  (14)

ρ₀−ρ=(ρ₀−ρ_(obm))βV ^(−γ=)β₂ V ^(−γ)  (15)

g=(GOR₀−GOR)b=GOR₀ βV ^(−γ)=β₃ V ^(−γ)  (16)

GOR₀ −ƒ=g=GOR₀ /βV ^(−γ)=β₃ V ^(−γ)  (16A)

where ƒ is GOR₀−(GOR₀−GOR)b, referred to as the ƒ function.

It is noted that the fitting exponent “−γ” may be kept the same for OD,density, and GOR fitting, which may make fitting more robust and/orreliable.

Taking the logarithm on both sides of Equations (14)-(16A) results inEquations (17), (18), (19), (19A), and (19B), set forth below.

ln|OD_(0i)−OD_(i)|=−γ ln V+ln β_(1i)  (17)

ln |ρ₀−ρ|=−γ ln V+ln β₂  (18)

ln|(GOR₀−GOR)b|=−γ ln V+ln β₃  (19)

ln|g|=−γ ln V+ln β₃  (19A)

ln|(GOR₀−ƒ)|=−γ ln V+ln β₃  (19B)

Hence, in log-log plots, one can obtain a linear relation in a selectedinterval of pumped volume or pumping time, such that the slope is γ.

FIGS. 5 and 6 depict example contamination cleanup curves for OBMfiltrate cleanup during oil sampling utilizing a radial unfocusedsampling probe (solid line 60) and an extra-large diameter unfocusedsampling probe (dashed line 61), including example contamination cleanupversus pumped volume (FIG. 5) and pumping time (FIG. 6). Unfocusedsampling probes have, at a minimum, a single inlet flowline connectingone or more probe inlets to the internal hydraulic circuitry of thedownhole tool, whereas focused sampling probes have two inlet flowlines(sample and guard) connecting the corresponding sample and guard inletsof the probe to the internal hydraulic circuitry of the downhole tool.Contamination in FIGS. 5 and 6 is plotted as a volume fraction (0=pureoil; 1=pure filtrate). FIGS. 5 and 6 also include a dotted line 62demarking a contamination level of five percent.

To perform the fitting, if a radial unfocused sampling probe isutilized, the slope γ may initially be set at ⅔ (0.667) when ν_(obm) isless than about ten percent, such as when late time cleanup data isutilized. If an unfocused single probe is utilized, the slope γ mayinitially be set at 5/12, at least during early time cleanup. However,if ν_(obm) is less than about three percent (among other examples withinthe scope of the present disclosure), such as during late time cleanup,setting the slope γ at ⅔ may obtain more reliable results for end pointsof the native formation fluid, as shown in FIGS. 5 and 6. However, otherinitial values for γ are also within the scope of the presentdisclosure. The log-log plot may also give a visual view of the fittingresults in the selected interval, as shown in FIGS. 5 and 6.

FIG. 7 schematically depicts an extra-large diameter unfocused samplingprobe 64, such as may correspond to line 62 in FIGS. 5 and 6, andseveral other sampling probes that may be utilized according to one ormore aspects of the present disclosure. The extra-large diameterunfocused sampling probe 64 may have a surface flow area of about 2.01in². Other unfocused sampling probes depicted in FIG. 7 include astandard sampling probe 65 that may have a surface flow area of about0.15 in², and a large diameter sampling probe 66 that may have a surfaceflow area of about 0.85 in². One or more aspects of the presentdisclosure may also be utilized in conjunction with a focused samplingprobe 68, such as may have a total (sample plus guard) surface flow areaof about 1.01 in². However, the sampling probes 64-66 and 68 depicted inFIG. 7 are merely examples, and other focused and unfocused samplingprobes having other surface flow areas are also within the scope of thepresent disclosure.

FIG. 8 depicts example contamination cleanup curves for a radial focusedsampling probe, and FIG. 9 depicts example contamination cleanup curvesfor a focused sampling probe, such as the focused sampling probe 68shown in FIG. 7. As shown in FIGS. 8 and 9, when a focused samplingprobe is utilized, the fluid in the guard flowline (dashed curves) hassimilar behavior to fluid obtained with an unfocused single samplingprobe, and the fluid in the guard line of the radial focused samplingprobe has similar behavior to fluid obtained with a radial unfocusedsampling probe. The late time guard flowline may thus correspond to aslope γ of ⅔. FIGS. 8 and 9 also depict that when the fluid in thesample flowline (solid curves) reaches low contamination, and there isan approximate linear relation between ν_(obm) and V (or t) for thesample flowline in the log-log plot, the fluid in the guard flowline(dashed curves) still has very high OBM filtrate contamination. Thus,the sample flowline cleanup data may be utilized for the power functionfitting, and the slope γ may be treated as an adjustable parameter. Forexample, the slope γ may be much greater than ⅔.

It is noted that an increase in the value of the slope γ indicates afaster decrease in ν_(obm) (faster cleanup). Thus, the smaller the valueof the slope γ, the more conservative the ν_(obm) estimation (the biggerν_(obm)).

FIG. 10 is a flow-chart diagram of at least a portion of a method (100)depicting a generalized workflow for OCM according to one or moreaspects of the present disclosure. The method (100) includes inputting(104) fluid parameters and other cleanup data obtained DFA utilizing adownhole sampling tool. The method (100) may also include conveying(108) the downhole sampling tool in a wellbore to a subterraneanformation penetrated by the wellbore, and operating (112) the downholesampling tool and/or surface equipment in communication with thedownhole sampling tool to obtain the cleanup data. The downhole samplingtool and/or surface equipment may then perform the cleanup data input(104) and/or the following actions, whether autonomously or inconjunction with a human operator's actions.

The cleanup data may include baseline-corrected OD_(i), GOR, massdensity ρ, pumped volume V (or pumping time t), and composition data,among other examples. An example of cleanup data for gas condensate isdepicted in FIG. 11, in which apparent GOR (ft³/bbl) is depicted assolid line 70, apparent density (g/cm³) is depicted as solid line 71,and multi-channel OD (unit-less) is depicted as lines 72, each as afunction of pumped volume V (or pumping time t).

The cleanup data may be denoised (116) utilizing a filter or statisticmethod, such as the student-test method. It is noted that spikes mayoccur when pumpout starts and sample bottles are being filled.

The start of linear behavior may then be determined (120) by, forexample, selecting an interval where the linear relationships hold incross plots. Such interval may be different from the fitting intervaldescribed below. The start of linear behavior may be determined (120) byvisual inspection of the cleanup data by a human operator. However, thestart of linear behavior may also or instead be determined (120) by theprocessing means of the downhole sampling tool, the surface equipment,and/or other equipment. For example, the early time cleanup data may becompared to a linear fitting of the cleanup data, such that linearitymay be determined (120) to have started when an R2 comparison of thecleanup data to the linear fitting is at least about 0.9. However, othermethods for determining (120) the start of linear behavior are alsowithin the scope of the present disclosure.

The molecular weight of gas M_(gas) may then be determined (124). Forexample, the molecular weight of gas M_(gas) may be determined (124)based on composition data input (104) with the cleanup data. However,other methods for determining (124) the molecular weight of gas M_(gas)are also within the scope of the present disclosure.

The method (100) also includes determining (128) an estimated stock tankoil density ρ_(STOStd) utilizing the cleanup data. For example, thestock tank oil density ρ_(STOStd) may be determined by one or more ofthe methods described above with respect to Equations (4)-(6). The stocktank oil density ρ_(STOStd) may also be determined (128) utilizing DFAdata from a previously analyzed well if the same type of OBM wasutilized.

The method (100) may also include estimating (132) GOR₀, the GOR of thenative formation fluid. For example, GOR₀ may be estimated (132)utilizing one or more of Equations (12)-(19B) set forth above.

B_(o), the formation volume factor of the native formation fluid, maythen be determined (136) utilizing one or more of the determined (124)molecular weight of gas M_(gas), the determined (128) stock tank oildensity ρ_(STOStd), and the estimated (132) GOR of the native formationfluid GOR₀. For example, B_(o) may be determined (136) utilizingEquation (4), assuming the formation volume factor of OBM filtrate inthe sampled fluid B_(oobm) is equal to one (1). The shrinkage factor bmay then be determined (140), such as via utilization of Equation (3)set forth above. The ƒ and g function may then be determined (144), suchas may include utilizing Equation (12) set forth above. The powerfunction fitting parameters described above, including the slope γ, maythen be determined (148).

Thereafter, OD_(i) and ρ versus the ƒ function may be plotted todetermine (152) their linear relationships. However, OD may not be shownin the crossplot vs. the ƒ function or in the OD-based contaminationplot if there is insufficient contrast, because both filtrate andcondensate are colorless. But if contrast exists (e.g., at an oilstation), OD may be utilized.

FIG. 12 depicts an example plot for a gas condensate, including ρ (g/cc)versus apparent GOR (solid line 73), and ρ versus the ƒ function (dashedline 74), and the corresponding determined linear relationships. It isnoted that ρ (g/cc) versus apparent GOR is included to show themagnitude of the error if GOR is used instead of the ƒ function.

Determining (152) the linear relationships may utilize Equations (20)and (21) set forth below.

ρ=a+b ₁ƒ  (20)

OD_(i) =c _(i) +d _(i)ƒ  (21)

The linear relationships determined (152) utilizing Equations (20) and(21) may then be utilized to extrapolate (156) the ƒ function to zero todetermine one or more properties of the OBM filtrate, such as OD_(obmi)and ρ_(obm), as set forth below in Equations (22) and (23).

ρ_(obm) =a  (22)

OD_(obmi) =c _(i)  (23)

However, in some environments, such as when oil GOR is very low, thedensity vs. OD plot and the ƒ function vs. OD plot may also beextrapolated to zero to obtain the filtrate properties.

It is also noted that if dual flowlines (guard and sample) are utilizedfor a focused sampling probe, the sample flowline may have substantiallyless OBM filtrate contamination relative to that of the guard flowline.Accordingly, the sample flowline may have a substantially larger GORrelative to that of the guard flowline. Therefore, more robust linearrelationships and extrapolation in plots of density (or OD) versus the ƒfunction may be obtained utilizing dual flowline information.

The linear relationships determined (152) utilizing Equations (20) and(21) may also be utilized to extrapolate (160) the g function todetermine one or more properties of the native formation fluid (absentOBM filtrate contamination), such as OD_(0i) and ρ₀, as set forth belowin Equations (24) and (25).

ρ₀ =a+b ₁GOR₀=ρ_(obm) +b ₁GOR₀  (24)

OD_(0i) =c _(i) +d _(i)GOR₀=OD_(obmi) +d _(i)GOR₀  (25)

Similarly, with a density vs. OD plot, the oil density may be determinedif the oil OD is known. Thus, if one of oil density, oil GOR, and oil ODis known, the other two can be determined.

In the gas condensate example depicted in FIG. 12, ρ_(obm)=0.8065 g/ccand ρ₀=0.3682 g/cc (the fitted GOR₀=18943 scf/bbl), as determined fromthe linear relationships shown therein. The density from the densityfitting itself is 0.3721 g/cc. Both densities are close, differing byabout one percent.

The method (100) may also include a comparison (164) of the properties(e.g., OD_(0i), ρ₀, OD_(obmi), and ρ_(obm)) of the pure native formationfluid and pure OBM filtrate extrapolated from the linear relationshipwith those from a power function fitting that utilizes the determined(148) fitting parameters. If they are not sufficiently close (e.g.,within about five or ten percent of each other), the root cause of thediscrepancy may be investigated to determine (166) which endpoints aremore reliable, those of the interpolation or those of the power fitting.If they are sufficiently close, the end points may then be output (168).For example, such output (168) may include OD_(0i), ρ₀, GOR₀, and m_(0i)of the native formation fluid, and OD_(obmi) and ρ_(obm) of the OBMfiltrate.

Thereafter, the contamination level in volume fraction on the liveformation fluid basis may be estimated (172) utilizing Equation (13).The example for gas condensate is depicted in FIGS. 13 and 14, depictingtwo example samples taken at different contamination levels. FIG. 13shows the g function from the model and g function with measured datainput. FIG. 14 shows modeled density and measured density. Both plotsshow the volumes at which samples are taken. The late time cleanup(e.g., when ν_(obm) is less than about three percent) follows a slope γof ⅔. Uncertainty analysis may then performed (176), includingdiscriminating those with large uncertainty from multiple sensors.

The method (100) may also include averaging (180) the contaminationlevels from the g function and density fitting, and/or selecting (184)the contamination level with the least uncertainty. The averaged (180)and/or selected (184) contamination level in volume fraction on the livefluid basis may then be converted (188), such as to that in weightfraction on the live fluid basis via Equation (26) set forth below.

$\begin{matrix}{w_{obm} = {\frac{v_{obm}\rho_{obm}}{\rho} = {{\frac{\rho_{obm}}{\rho}\frac{{OD}_{0i} - {OD}_{i}}{{OD}_{0i} - {OD}_{obmi}}} = {{\frac{\rho_{obm}}{\rho}\frac{\rho_{0} - \rho}{\rho_{0} - \rho_{obm}}} = {\frac{\rho_{obm}}{\rho}\frac{\left( {{GOR}_{0} - {GOR}} \right)b}{{GOR}_{0}}}}}}} & (26)\end{matrix}$

The conversion (188) may also include converting to contamination levelin volume fraction on the STO basis, such as via Equation (27) set forthbelow.

$\begin{matrix}{v_{obmSTO} = {\frac{{OD}_{0i} - {OD}_{i}}{\left( {{OD}_{0i} - {OD}_{obmi}} \right)b} = {\frac{\rho_{0} - \rho}{\left( {\rho_{0} - \rho_{obm}} \right)b} = \frac{{GOR}_{0} - {GOR}}{{GOR}_{0}}}}} & (27)\end{matrix}$

The conversion (188) may also include converting to contamination levelin weight fraction on the STO basis, such as via Equation (28) set forthbelow.

$\begin{matrix}{w_{obmSTO} = {{\frac{\rho_{obmStd}}{\rho_{STOStd}}\frac{{OD}_{0i} - {OD}_{i}}{\left( {{OD}_{0i} - {OD}_{obmi}} \right)b}} = {{\frac{\rho_{obmStd}}{\rho_{STOStd}}\frac{\rho_{0} - \rho}{\left( {\rho_{0} - \rho_{obm}} \right)b}} = {\frac{\rho_{obmStd}}{\rho_{STOStd}}\frac{{GOR}_{0} - {GOR}}{{GOR}_{0}}}}}} & (28)\end{matrix}$

The method (100) may also include determining (192) the uncontaminatedformation fluid compositions, such as by assuming that OBM filtrate isheavier than C6, thus resulting in Equations (29) and (30) set forthbelow.

$\begin{matrix}{{m_{0i} = \frac{m_{i}}{1 - w_{obm}}},{i = C_{1}},C_{2},C_{3},C_{4},C_{5},{CO}_{2}} & (29) \\{m_{{0C\; 6} +} = \frac{m_{{C\; 6} +} - w_{obm}}{1 - w_{obm}}} & (30)\end{matrix}$

where m is the composition in weight fraction.

The power function fitting of composition versus pumpout volume (ortime) may also be applied to obtain the composition of uncontaminatedfluid by extrapolating pumpout volume or time to infinity. This processmay also be applied to other fluid properties, such as compressibility,formation volume factor, saturation pressure, and viscosity.

Because OBM contamination level and OBM filtrate properties are known,the mixing rules may be employed to compute properties of the purevirgin hydrocarbon fluid as well. For example, if density contrast issmall, it may be difficult to obtain good fitting for density.Therefore, the density-mixing rule may be utilized to calculate densityof the pure virgin hydrocarbon fluid.

The method (100) may also include converting (196) density and viscosityfrom flowline to formation conditions. That is, flowline conditions maybe very different from formation conditions, such as if sensors arepositioned downstream of the pumpout module where pressure issubstantially greater than formation pressure. There are at least twoways to do this conversion (196).

First, an EoS (or/and viscosity) model may be established based onm_(i0), such that density (ρ₀) or/and viscosity may be matched atflowline conditions, and density (ρ₀) or/and viscosity may be predictedat formation conditions. Second, based on the density versus pressurerelation during sampling, one can obtain compressibility versuspressure, such as set forth below in Equation (31).

$\begin{matrix}{c = {{\frac{1}{\rho}\frac{\partial\rho}{\partial P}} = \frac{{\partial\ln}\; \rho}{\partial P}}} & (31)\end{matrix}$

where c is compressibility, which is a function of pressure. This leadsto Equation (32) set forth below.

$\begin{matrix}{\left. {\ln \mspace{11mu} \rho_{0}} \right|_{formation} = \left. {\ln \mspace{11mu} \rho_{0}} \middle| {}_{flowline}{+ {\int_{P\_ {flowline}}^{P\_ {formation}}{{c(P)}{dP}}}} \right.} & (32)\end{matrix}$

The method (100) described above, and/or related methods within thescope of the present disclosure, may include a power law extrapolationon the ƒ function. For example, this may be substantially similar to theextrapolation of the g function as described above, but perhaps shiftedby a constant value (e.g., GOR₀). This may not add information over theg function extrapolation—such as in implementations in which the ƒfunction or the g function would be selected with similar results—butmay be a matter of preference based on available data, preliminaryresults, and/or other factors.

FIG. 15 is a flow-chart diagram of an example of a method fordetermining (148) the fitting parameters described above with respect toFIG. 10. The method (148) may include selecting (204) start and endpoints of an interval to be utilized for power function fitting. Suchselection (204) may be based on a flow regime identification method,among other examples.

The slope γ to be utilized for the power function fitting may then beinitially selected (208). For example, if an unfocused radial samplingprobe is utilized, and if ν_(obm) is less than about ten percent, avalue of ⅔ may be initially selected (208) for the slope γ. If anunfocused single sampling probe (such as the unfocused sampling probes64-66 shown in FIG. 7) is utilized, a value of 5/12 may be initiallyselected (208) of the slope γ for early time cleanup, and a value of ⅔may be initially selected (208) for the slope γ for late time cleanup(such as when ν_(obm) is less than about three percent. For focusedsampling probes, whether radial or otherwise, when the sample linereaches low contamination, there is an approximate linear relationbetween ν_(obm) and V (or t) in the log-log plot. However, the guardline may still have very high contamination. Thus, the sample linecleanup data may be utilized for the power function fitting, and theslope γ may be treated as an adjustable parameter that is much largerthan one (e.g., γ>>⅔). Initial values of the reservoir fluid propertiesmay then be assumed (212), such as OD_(0i), ρ₀, GOR₀, and γ if γ is notfixed.

An iteration of linear regression may then be performed (216), such asby utilizing Equations (17)-(19B). After each iteration of the linearregression is performed (216), the correlation coefficients R2 may beassessed (220) to determine if a best (maximum) correlation has beenobtained. If they are not optimized, OD_(0i), ρ₀, GOR₀, and γ (if γ isadjustable) may be updated. Example results for gas condensate aredepicted in FIGS. 16 and 17. It is noted, however, that thisoptimization may also be performed via non-linear regression methods.

If it is determined (220) that the initial values for the reservoirfluid properties are optimized, a check of whether the best fitting isachieved may then be performed (224) by comparing endpoints of the purereservoir fluid from different intervals. If it is determined (224) thatbest fitting has not been achieved, a different fitting interval may beselected (204), and the regression or other optimization may berepeated. If it is determined (224) that best fitting has been achieved,the current values for the reservoir fluid properties are utilized(228).

The objective of this plot is to visualize when the measured data(manipulated as per Equations (17), (18), and/or (19)) forms a straightline with slope γ so that the power law can be fit on that section ofdata that exhibits constant power law behavior, such as to avoid fittinga constant power law model on a varying power law dataset. When thestart-fit is changed, the OD_(0i), ρ₀, GOR₀, and γ (if γ is adjustable)also change, hence the iterative process described above.

One or more aspects of the method (100) and/or other aspects describedabove may find application in various DFA settings. For example, it canbe assumed that a fluid is in a single-phase at downhole conditions. Thephase may be liquid (“oil”) or gas (“gas condensate”). The fluid may beflashed from downhole conditions to standard conditions (e.g., 14.7 psiaand 60 degrees F.), resulting in flashed liquid (“stock-tank oil” orSTO) and flashed gas. That is, the downhole (“live”) fluid may have avolume V, a density ρ (known as apparent density), and a mass g, and maycomprise OBM having a volume ν_(obm). The flashed gas may have a volumeV_(g), a molecular weight MW_(g), a mass m_(g), a number of moles N_(g),and a mole ratio n_(g) equal to N_(g)/(N_(g)+N_(STO)). The STO portionof the flashed liquid may have a volume V_(STO), a density ρ_(STO), amolecular weight MW_(STO), a mass M_(STO), and a number of molesN_(STO), and the OBM portion of the flashed liquid may have a volumeV_(obmSTO). As with the description above, the subscripts 0 and obm inthe following description designate properties of the pure formationfluid and the pure OBM filtrate, respectively.

According to the definition of the single-stage flash FVF describedabove, the FVF of the native formation fluid (B_(o)) may be determinedas set forth below in Equation (33).

$\begin{matrix}{B_{o} = {\frac{V}{V_{STO}} = {{\left( {1 + {\frac{GOR}{23.69}\frac{{MW}_{g}}{\rho_{STO}}}} \right)\frac{\rho_{STO}}{\rho}} = {\frac{\rho_{STO}}{\rho} + {\frac{GOR}{23.69}\frac{{MW}_{g}}{\rho}}}}}} & (33)\end{matrix}$

Equation (33) is similar to Equation (4) set forth above.

STO-based OBM filtrate contamination in volume fraction (ν_(obmSTO)) mayalso be derived in terms of the GOR definition as set forth below inEquation (34).

$\begin{matrix}{v_{obmSTO} = {\frac{V_{obmSTO}}{V_{STO}} = \frac{{GOR}_{0} - {GOR}}{{GOR}_{0}}}} & (34)\end{matrix}$

Equation (34) is similar to Equation (5) set forth above.

Applying the density mixing rule (e.g., Equation (13) set forth above),and assuming that mixing the formation fluid with the OBM filtrate isideal (e.g., that no excess volume is generated during mixing), theapparent density ρ may be expressed as set forth below in Equation (35).

ρ=ν_(obm)ρ_(obm)+(1−ν_(obm))ρ₀  (35)

Rearranging Equation (35) may yield the live-fluid-based OBM filtratecontamination in volume fraction ν_(obm), as set forth below in Equation(36).

$\begin{matrix}{v_{obm} = {\frac{V_{obm}}{V} = \frac{\rho_{0} - \rho}{\rho_{0} - \rho_{obm}}}} & (36)\end{matrix}$

Equation (36) is similar to Equation (11) set forth above.

Based on one unit volume of the OBM-filtrate-contaminated fluid, ν_(obm)can be converted to STO-based OBM filtrate contamination in volumefraction (ν_(obmSTO)) by formation volume factors. BecauseV_(obmSTO)=V_(obm)/B_(oobm) (whereB_(oobm)=V_(obm)/V_(obmSTO)=ρ_(obmSTO)/ρ_(obm)) and V_(STO)=V/B_(o),converting Equation (36) and substituting it into Equation (34) resultsin Equation (37) set forth below.

$\begin{matrix}{{v_{obmSTO} = {\frac{v_{obm}B_{o}}{B_{oobm}} = {{\frac{\rho_{0} - \rho}{\rho_{0} - \rho_{obm}}\frac{B_{o}}{B_{oobm}}} = \frac{{GOR}_{0} - {GOR}}{{GOR}_{0}}}}},{b = \frac{B_{oobm}}{B_{o}}}} & (37)\end{matrix}$

where b (=B_(oobm)/B_(o)) is the shrinkage factor.

The density mixing rule can also be directly applied to STO, resultingin Equation (38) set forth below.

ρ_(STO)=ν_(obmSTO)ρ_(obmSTO)+(1−ν_(obmSTO))ρ_(0STO)  (38)

Rearranging Equation (38) and substituting it into Equation (34) resultsin Equation (39) set forth below.

$\begin{matrix}{v_{obmSTO} = {\frac{\rho_{0{STO}} - \rho_{STO}}{\rho_{0{STO}} - \rho_{obmSTO}} = \frac{\left( {{GOR}_{0} - {GOR}} \right)}{{GOR}_{0}}}} & (39)\end{matrix}$

where ρ_(0STO) and ρ_(obmSTO) are respectively the STO density of theformation fluid and OBM filtrate, at standard conditions.

Rearranging Equation (33) may then result in Equation (40) set forthbelow.

$\begin{matrix}{{{B_{o}\rho} - \rho_{STO}} = \frac{{GOR} \cdot {MW}_{g}}{23.69}} & (40)\end{matrix}$

Rearranging Equation (37) may then result in Equation (41) set forthbelow.

$\begin{matrix}{{B_{o}\rho} = {{\rho_{0}B_{o}} - \frac{\left( {{GOR}_{0} - {GOR}} \right)\left( {\rho_{0} - \rho_{obm}} \right)B_{oobm}}{{GOR}_{0}}}} & (41)\end{matrix}$

Rearranging Equation (39) may then result in Equation (42) set forthbelow.

$\begin{matrix}{\rho_{STO} = {\rho_{0{STO}} - \frac{\left( {{GOR}_{0} - {GOR}} \right)\left( {\rho_{0{STO}} - \rho_{obmSTO}} \right)}{{GOR}_{0}}}} & (42)\end{matrix}$

Operating both sides of Equations (40)-(42) by adding Equations (40) and(42) and subtracting Equation (41) may then result in Equation (43) setforth below.

$\begin{matrix}{{{B_{o}\rho} - \rho_{STO} + \rho_{STO} - {B_{o}\rho}} = {\frac{{GOR} \cdot {MW}_{g}}{23.69} + \rho_{0{STO}} - \frac{\left( {{GOR}_{0} - {GOR}} \right)\left( {\rho_{0{STO}} - \rho_{obmSTO}} \right)}{{GOR}_{0}} - {\rho_{0}B_{o}} + \frac{\left( {{GOR}_{0} - {GOR}} \right)\left( {\rho_{0} - \rho_{obm}} \right)B_{oobm}}{{GOR}_{0}}}} & (43)\end{matrix}$

The left-hand side of Equation (43) is equal to zero, resulting inEquation (44) set forth below.

$\begin{matrix}{0 = {\frac{{GOR} \cdot {MW}_{g}}{23.69} + \rho_{0{STO}} - \frac{\left( {{GOR}_{0} - {GOR}} \right)\left( {\rho_{0{STO}} - \rho_{obmSTO}} \right)}{{GOR}_{0}} - {\rho_{0}B_{o}} + \frac{\left( {{GOR}_{0} - {GOR}} \right)\left( {\rho_{0} - \rho_{obm}} \right)B_{oobm}}{{GOR}_{0}}}} & (44)\end{matrix}$

Rearranging Equation (44) may then result in Equation (45) set forthbelow.

$\begin{matrix}{{\rho_{0}B_{o}} = {{\frac{{GOR} \cdot {MW}_{g}}{23.69} + \rho_{0{STO}} - \left( {\rho_{0{STO}} - \rho_{obmSTO}} \right) + \frac{{GOR}\left( {\rho_{0{STO}} - \rho_{obmSTO}} \right)}{{GOR}_{0}} + {\left( {\rho_{0} - \rho_{obm}} \right)B_{oobm}} - \frac{{{GOR}\left( {\rho_{0} - \rho_{obm}} \right)}B_{oobm}}{{GOR}_{0}}} = {{{\left\lbrack {\frac{{MW}_{g}}{23.69} + \frac{\left( {\rho_{0{STO}} - \rho_{obmSTO}} \right)}{{GOR}_{0}} - \frac{\left( {\rho_{0} - \rho_{obm}} \right)_{oobm}}{{GOR}_{0}}} \right\rbrack {GOR}} + \rho_{obmSTO} + {\left( {\rho_{0} - \rho_{obm}} \right)B_{oobm}}} = {\quad{{\left\lbrack {\frac{{MW}_{g}}{23.69} + \frac{\left( {\rho_{0{STO}} - \rho_{obmSTO}} \right)}{{GOR}_{0}} - \frac{\left( {{\rho_{0}B_{oobm}} - \rho_{obmSTO}} \right)}{{GOR}_{0}}} \right\rbrack {GOR}} + \rho_{obmStd} + \left( {{\rho_{0}B_{oobm}} - \rho_{obmSTO}} \right)}}}}} & (45)\end{matrix}$

Obtaining Equation (45) included using Equation (46) set forth below,based on the assumption that OBM is not present in the gas phase.

ρ_(obmSTO) =B _(oobm)ρ_(obm)  (46)

Rearranging Equation (45) may then result in Equation (47) set forthbelow.

$\begin{matrix}{{\rho_{0}B_{o}} = {{\left\lbrack {\frac{{MW}_{g}}{23.69} + \frac{\left( {\rho_{0{STO}} - {\rho_{0}B_{oobm}}} \right)}{{GOR}_{0}}} \right\rbrack {GOR}} + {\rho_{0}B_{oobm}}}} & (47)\end{matrix}$

Therefore, the FVF may be expressed as set forth below in Equation (48).

$\begin{matrix}{B_{o} = {{{\left\lbrack {\frac{{MW}_{g}}{23.69\rho_{0}} + \frac{\left( {\rho_{0{STO}} - {\rho_{0}B_{oobm}}} \right)}{\rho_{0}{GOR}_{0}}} \right\rbrack {GOR}} + B_{oobm}} = {{\frac{B_{o\; 0} - B_{oobm}}{{GOR}_{0}}{GOR}} + B_{oobm}}}} & (48)\end{matrix}$

The reciprocal of the shrinkage factor b may then be written as setforth below in Equation (49).

$\begin{matrix}{\frac{1}{b} = {\frac{B_{o}}{B_{oobm}} = {{{\frac{\frac{B_{o\; 0}}{B_{oobm}} - 1}{{GOR}_{0}}{GOR}} + 1} = {{\frac{\frac{1}{b_{0}} - 1}{{GOR}_{0}}{GOR}} + 1}}}} & (49)\end{matrix}$

At a specified DFA station, the OBM filtrate properties (e.g., B_(oobm))and the formation fluid properties (e.g., GOR₀, ρ₀, and ρ_(0STO)) arefixed during the cleanup phase, because the OBM filtrate is assumed tonot be present in the gas (vapor) phase, such that MW_(g) remainsconstant. Therefore, the coefficient of GOR on the right-hand side ofEquation (49) is constant. Thus, the reciprocal of the shrinkage factoris linearly associated with GOR, and also passes through the point of 1(interception=1) at GOR=0, where it is the OBM filtrate (B_(o)=B_(oobm)and 1/b=1). Accordingly, Equation (49) may be rewritten as set forthbelow in Equation (50).

$\begin{matrix}{\frac{1}{b} = {\frac{B_{o}}{B_{oobm}} = {{k_{1}{GOR}} + 1}}} & (50)\end{matrix}$

The coefficient k₁ (slope) is given by Equation (51) set forth below.

$\begin{matrix}{k_{1} = {\left\lbrack {\frac{{MW}_{g}}{23.69\rho_{0}B_{oobm}} + \frac{\left( {\rho_{0{STO}} - {\rho_{0}B_{oobm}}} \right)}{\rho_{0}{GOR}_{0}B_{oobm}}} \right\rbrack = {\frac{\frac{B_{o\; 0}}{B_{oobm}} - 1}{{GOR}_{0}} = \frac{\frac{1}{b_{0}} - 1}{{GOR}_{0}}}}} & (51)\end{matrix}$

where the interception=1 in the plot of 1/b versus GOR.

To confirm this linear relation, experiments were conducted by mixing aheavy oil (HO), a black oil (BO), and a gas condensate (GC) with threetypes of OBM filtrate (named as Muds 1, 2, and 3) at different OBMcontamination levels. The results are shown in FIG. 18, in which it canbe seen that 1/b versus GOR is linear over an entire OBM contaminationrange, from the pure OBM filtrate to the native formation fluid.

Rearranging Equation (42) may then result in Equations (52) and (53) setforth below.

$\begin{matrix}{\rho_{STO} = {{\rho_{obmSTO} + \frac{\left( {\rho_{0{STO}} - \rho_{obmSTO}} \right){GOR}}{{GOR}_{0}}} = {\rho_{obmSTO} + {k_{2}{GOR}}}}} & (52) \\{\mspace{79mu} {k_{2} = \frac{\left( {\rho_{0{STO}} - \rho_{obmSTO}} \right)}{{GOR}_{0}}}} & (53)\end{matrix}$

The STO density is linearly related to GOR during cleanup. Thus, onceSTO density is known with respect to GOR and k₂, the STO density may bepopulated using GOR. If two points are known, the linear relationshipmay be determined.

FIG. 19 depicts laboratory data for the heavy oil (HO) and black oil(BO) mixed with three types of OBM filtrates. It can be seen in FIG. 19that the STO density is linearly related with GOR over the entire OBMcontamination range, from the pure OBM filtrate to the native formationfluid.

In the following description, the superscript * represents an initialestimate or an intermediate determination of the corresponding value,and the corresponding parameters without the superscript * represent a“final” determined output, in a sense that is at least analogous to aniterative process. For example, ρ*_(STO) may be an initial estimate orintermediate determination of the density of STO, whereas ρ_(STO) may bethe ultimately determined density of STO.

The linear relations can be applied to obtain logs of FVF and APIgravity (STO density) and those of the native formation fluids. Forexample, FIG. 20 is a flow-chart diagram of at least a portion of amethod (400) representing an example workflow for such application. Themethod (400) includes inputting (404) cleanup data, such as may includeDFA composition data (e.g., weight fractions of C1, C2, C3-05, C6+, andCO2, although other composition schemes are also within the scope of thepresent disclosure), density ρ, and GOR, each with respect to pumpedvolume V or pumping time t. The method (400) may also include conveying(408) the downhole sampling tool in a wellbore to a subterraneanformation penetrated by the wellbore, and operating (412) the downholesampling tool and/or surface equipment in communication with thedownhole sampling tool to obtain the cleanup data. The downhole samplingtool and/or surface equipment may then perform the cleanup data input(404) and/or the following actions, whether autonomously or inconjunction with a human operator's actions.

The cleanup data may then be denoised (414) utilizing a filter orstatistic method, such as the student-test method. The density ρ*_(STO)may then be determined (416) from the input (904) or denoised (914)data, such as via artificial neural network (ANN) processing (such asmay be a by-product of the GOR algorithm). An initial estimate ofρ*_(obm), may also be determined (420), such as from prior information.The determined (416) density ρ*_(STO) as a function of GOR may then befit to determine (424) ρ_(obmSTO) and k₂ in Equation (52) set forthabove. The density ρ_(STO) may then be determined (428) using Equation(52) based on GOR, thus obtaining the ρ_(STO) log. The API gravity maythen be determined (432) to provide an API gravity log, such as byutilizing Equation (54) set forth below.

$\begin{matrix}{{API} = {\frac{141.5}{\rho_{STO}} - 131.5}} & (54)\end{matrix}$

The composition and/or ANN processing may then be utilized to determine(436) MW_(g). An initial B*_(o) may then be estimated (440) from MW_(g)and ρ_(STO), such as by utilizing Equation (33) set forth above. B_(o)from the estimated B*_(o) data may then be fit, such as by usingEquation (50) set forth above, to determine (444) k₁. B_(o) based on GORmay then be determined (448), such as by utilizing Equation (50) setforth above, thus providing the B_(o) log. The above-described OCMprocess may then be utilized to obtain (452) GOR₀, such as by performinga portion of the method (100) shown in FIG. 10 B_(o0) and ρ_(STO0) maythen be determined (456), such as by substituting GOR₀ into Equations(50) and (52) set forth above.

An example for gas condensate is shown in FIG. 21, including exampleresults for g function (line 310) and the associated fitting (line 312)and example results for density (line 314) and the associated fitting(line 316). In the example shown in FIG. 21, GOR₀=18,943 scf/bbl, andρ₀=0.3721 g/cc. FIG. 22 shows the associated plot of 1/b versus GOR,demonstrating a substantially linear fitting, with an R² value closeto 1. In the example associated with FIGS. 21 and 22, k₁=4.702×10⁻⁴bbl/scf. FIGS. 23-25 show the related logs of STO density, API gravity,and FVF, respectively.

Continuing with this example, because GOR₀=18,943 scf/bbl, the nativeformation fluid STO density may be determined as 0.8244 g/cc (see FIG.23), the API gravity may be determined as 40.1° API (see FIG. 24), andthe FVF may be determined as 9.9 (see FIG. 25). The related laboratoryresults were in substantial agreement at 0.8227 g/cc, 40.5° API, and10.9, respectively.

FIG. 26 is a flow-chart diagram of at least a portion of a method (900)representing another example workflow according to one or more aspectsof the present disclosure. The method (900) may include plotting (905)1/b versus GOR and then obtaining (910) the linear relation with slopek₁. An OCM algorithm may then be utilized to obtain (915) GOR₀ and ρ₀ ofthe native formation fluid. Assuming B_(oobm)=1, Equation (51) may beutilized to obtain Equation (55) set forth below.

$\begin{matrix}{\rho_{0{STO}} = {\rho_{0} + \left( {{k_{1}\rho_{0}{GOR}_{0}} - \frac{{MW}_{g}{GOR}_{0}}{23.69}} \right)}} & (55)\end{matrix}$

Equation (53) set forth above may then be utilized to determine (920)k₂, because ρ_(obmSTO), ρ_(0STO), and GOR₀ are known. Equation (52) maythen be utilized to determine (925) ρ_(STO), and Equation (54) may thenbe utilized to determine (930) API gravity. The STO density and APIgravity versus pumped volume and/or pumping time (from the STO densityand API logs) may then be populated (935).

An example for black oil is shown in the plot of 1/b versus GOR depictedin FIG. 27. The slope k₁=3.9695×10⁻⁴ bbl/scf, ρ_(obmSTO)=0.807 g/cc,ρ₀=0.754 g/cc, and GOR₀=1,189 scf/bbl. Thus, the STO density of thenative formation fluid is ρ_(0STO)=0.9145 g/cc. This also permitsdetermining that k₂=9.0442×10⁻⁵ bbl/scf. Therefore, the STO density andAPI gravity logs may be populated, as shown in FIGS. 28 and 29.

The linear relations determined as described above may also be appliedto populate STO density and 1/b logs with GOR, time, and pumpout volume.Accordingly, one can obtain more reliable g function fitting and ƒfunction extrapolation, thus yielding more reliable endpoints of thepure OBM filtrate and pure formation fluid, as well as more reliable OBMfiltrate contamination.

FIG. 30 is a schematic view of at least a portion of a drilling system510 operable to drill a wellbore 526 into one or more subsurfaceformations 512. One or more aspects described above may be performed byor in conjunction with one or more aspects of the drilling system 510shown in FIG. 30.

A drilling rig 514 at the wellsite surface 516 is operable to rotate adrill string 518 that includes a drill bit 520 at its lower end. As thedrill bit 520 is rotated, a pump 522 pumps OBM downward through thecenter of the drill string 518 in the direction of the arrow 524 to thedrill bit 520. The OBM cools and lubricates the drill bit 520 and exitsthe drill string 518 through ports (not shown) in the drill bit 520. TheOBM then carries drill cuttings away from the bottom of the wellbore 526as it flows back to the wellsite surface 516 through an annulus 530between the drill string 518 and the formation 512, as shown by thearrows 528. At the wellsite surface 516, the return OBM is filtered andconveyed back to a mud pit 532 for reuse.

While a drill string 518 is illustrated in FIG. 30, it will beunderstood that implementations described herein may be applicable orreadily adaptable to work strings and wireline tools as well. Workstrings may include a length of tubing (e.g., coiled tubing) loweredinto the wellbore 526 for conveying well treatments or well servicingequipment. Wireline tools may include formation testing tools suspendedfrom a multi-conductor cable as the cable is lowered into the wellbore526 to measure formation properties at desired depths.

The location and environment of the drilling system 510 may varydepending on the formation 512 penetrated by the wellbore 526. Insteadof being a surface operation, for example, the wellbore 526 may beformed under water of varying depths, such as on an ocean bottomsurface. Certain components of the drilling system 510 may be speciallyadapted for underwater wells in such instances.

The lower end of the drill string 518 includes a bottom-hole assembly(BHA) 534, which includes the drill bit 520 and a plurality of drillcollars 536, 538. The drill collars 536, 538 may include variousinstruments, such as sample-while-drilling (SWD) tools that includesensors, telemetry equipment, and so forth. For example, the drillcollars 536, 538 may include logging-while-drilling (LWD) modules 540and/or measurement-while drilling (MWD) modules 542 that may compriseone or more of the probes 64-66 and/or 68 shown in FIG. 7 for obtaininga sample of fluid from the formation 512. The LWD modules or tools 540may include tools operable to measure formation parameters and/or fluidproperties, such as resistivity, porosity, permeability, sonic velocity,OD, pressure, temperature, and/or others. The MWD modules or tools 542may include tools operable to measure wellbore trajectory, boreholetemperature, borehole pressure, and so forth. The LWD modules 540 mayeach be housed in one of the drill collars 536, 538, and may eachcontain one or more logging tools and/or fluid sampling devices. The LWDmodules 540 include capabilities for measuring, processing, and/orstoring information, as well as for communicating with the MWD modules542 and/or with surface equipment such as, for example, a logging andcontrol unit 544. That is, the SWD tools (e.g., LWD and MWD modules 540,542) may be communicatively coupled to the logging and control unit 544disposed at the wellsite surface 516. In other implementations, portionsof the logging and control unit 544 may be integrated with downholefeatures.

The LWD modules 540 and/or the MWD modules 542 may include a downholeformation fluid sampling tool operable to selectively sample fluid fromthe formation 512. The drilling system 510 may be operable to determine,estimate, or otherwise obtain various properties associated with thesampled formation fluid. These properties may be determined within orcommunicated to the logging and control unit 544, such as for subsequentutilization as input to various control functions and/or data logs,including as described above for OCM purposes.

FIG. 31 is a schematic diagram of an embodiment of downhole equipment(equipment configured for operation downhole) operable to sample fluidfrom a formation, such as the formation(s) 512 shown in FIG. 30. Thedownhole equipment includes an example embodiment of a downholeformation fluid sampling tool 650, hereinafter referred to as thedownhole tool 650. The downhole tool 650 is conveyable within thewellbore 526 to the subsurface formation 512 and subsequently operableto sample formation fluid from the formation 512. In the illustratedembodiment, the downhole tool 650 is conveyed in the wellbore 526 via awireline 652. The downhole tool 650 may be suspended in the wellbore 526from a lower end of the wireline 652, which may be a multi-conductorcable spooled from a winch 654 at the surface. The wireline 652 may beelectrically coupled to wellsite surface equipment 656, such as tocommunicate various control signals and logging information between thedownhole tool 650 and the wellsite surface equipment 656. The wellsitesurface equipment 656 shown in FIG. 31 and the logging and control unit544 shown in FIG. 30, or functions thereof, may be integrated in asingle system at the wellsite surface 516.

The downhole tool 650 includes a probe module 658, a pumpout module 660,and a sample module 662, one or more of which may comprise, be part of,be substantially similar to, or otherwise have similar functionalityrelative to one or more of the SWD tools, LWD modules 540, and/or MWDmodules 542 shown in FIG. 30 and/or described above. However, otherarrangements and/or modules may make up the downhole tool 650.

The probe module 658 may comprise a probe 664 operable to engage theformation 512 and communicate fluid samples from the formation 512 intothe downhole tool 650. The probe 664 may be, comprise, or besubstantially similar to one or more of the probes 64-66 and/or 68 shownin FIG. 7. The probe module 658 may also comprise one or more settingmechanisms 666. The setting mechanisms 666 may include pistons and/orother apparatus operable to improve sealing engagement and thus fluidcommunication between the formation 512 and the probe 664. The probemodule 658 may also comprise one or more packer elements (not shown)that inflate or are otherwise operable to contact an inner wall of thewellbore 526, thereby isolating a section of the wellbore 526 forsampling. The probe module 658 may also comprise electronics, batteries,sensors, and/or hydraulic components used, for example, to operate theprobe 664 and/or the corresponding setting mechanisms 666.

The pumpout module 660 may comprise a pump 668 operable to create apressure differential that draws the formation fluid in through theprobe 664 and pushes the fluid through a flowline 670 of the downholetool 650. The pump 668 may comprise an electromechanical, hydraulic,and/or other type of pump operable to pump formation fluid from theprobe module 658 to the sample module 662 and/or out of the downholetool 650. The pump 668 may operate as a piston displacement unit (DU)driven by a ball screw coupled to a gearbox and an electric motor,although other types of pumps 668 are also within the scope of thepresent disclosure. Power may be supplied to the pump 668 via othercomponents located in the pumpout module 660, or via a separate powergeneration module (not shown). During a sampling period, the pump 668moves the formation fluid through the flowline 670 toward the samplemodule 662.

The pumpout module 660 may also include a spectrometer 672 operable tomeasure characteristics of the formation fluid as it flows through theflowline 670. The spectrometer 672 may be located downstream or upstreamof the pump 668. The characteristics sensed by the spectrometer 672 mayinclude OD of the formation fluid. Data collected via the spectrometer672 may be utilized to control the downhole tool 650. For example, thedownhole tool 650 may not operate in a sample collection mode until theformation fluid flowing through the flowline 670 exhibitscharacteristics of a clean formation fluid sample, as detected by orotherwise determined in conjunction with operation of the spectrometer672. A clean formation fluid sample contains a relatively low level ofcontaminants (e.g., drilling mud filtrate) that are miscible with theformation fluid when extracted from the formation 512. Suchcontamination level may be determined according to one or more of theaspects described above, including with respect to the methods shown inFIGS. 10, 20, and/or 26.

The sample module 662 may comprise one or more sample bottles 674 forcollecting samples of the formation fluid. Based on the OD and/or othercharacteristics of the formation fluid detected via sensors (e.g., thespectrometer 672) along the flowline 670, the downhole tool 650 may beoperated in a sample collection mode or a continuous pumping (cleanup)mode. When operated in the sample collection mode, valves (not shown)disposed at or near entrances of the sample bottles 674 may bepositioned to allow the formation fluid to flow into the sample bottles674. The sample bottles 674 may be filled one at a time, and once asample bottle 674 is filled, its corresponding valve may be moved toanother position to seal the sample bottle 674. When the valves areclosed, the downhole tool 650 may operate in a continuous pumping mode.

In the continuous pumping mode, the pump 668 moves the formation fluidinto the downhole tool 650 through the probe 664, through the flowline670, and then out of the downhole tool 650 through an exit port 676. Theexit port 676 may be a check valve that releases the formation fluidinto the annulus 530 of the wellbore 526. The downhole tool 650 mayoperate in the continuous pumping mode until the formation fluid flowingthrough the flowline 670 is determined to be clean enough for storing.That is, when the formation fluid is first obtained from the formation512, OBM filtrate that has been forced into the formation 512 via thedrilling operations may enter the downhole tool 650 along with theobtained formation fluid. After pumping the formation fluid for anamount of time, the formation fluid flowing through the downhole tool650 will provide a cleaner fluid sample of the formation 512 than wouldotherwise be available when first drawing fluid in through the probe664. For example, the formation fluid may be considered clean when theOD data from the spectrometer 672 is processed as described above andindicates that the formation fluid contains less than approximately 1%,5%, or 10% OBM filtrate contamination (by volume), although other valuesare also within the scope of the present disclosure.

The characteristics of the formation fluid measured by the spectrometer672 may be useful for performing a variety of evaluation and controlfunctions, in addition to determining when the formation fluid flowingthrough the flowline 670 is clean enough for storing. For example, datamay be collected from the spectrometer 672 and/or other sensors withinthe downhole tool, such as a density sensor, a viscosity sensor, apressure sensor, a temperature sensor, and/or a saturation pressuresensor, among others. The collected data may be utilized to estimate aformation volume factor of the contaminated formation fluid, as well asdensity, optical density, GOR, compressibility, saturation pressure,viscosity, and/or mass fractions of compositional components of thecontaminated formation fluid and/or contaminants therein (e.g., OBMfiltrate), among others.

FIG. 32 is a schematic diagram of the spectrometer 672 and acontrol/monitoring system 690 that may be utilized to estimate ordetermine one or more of such properties. The spectrometer 672 maycomprise a light source 692 and a detector 694 disposed on oppositesides of the flowline 670 through which the formation fluid flows, asindicated by arrow 696. The spectrometer 672 may be part of the downholetool 650, and may be located at various possible locations along theflowline 670 that directs the formation fluid through the downhole tool650. Although a single light source 692 is depicted in the example shownin FIG. 32, the spectrometer 672 may include additional light sources692. The detector 694 may sense the light that passes through theformation fluid in the flowline 670.

The detector 694 may include one or more detector elements 698 that mayeach be operable to measure the amount of light transmitted at a certainwavelength. For example, the detector elements 698 may detect the lighttransmitted from the visible to near-infrared within a range of 1, 5,10, 20, or more different wavelengths ranging between about 400 nm andabout 2200 nm. However, other numbers of wavelengths (corresponding tothe number of detector elements) and other ranges of wavelengths arealso within the scope of the present disclosure. For example, opticalcharacteristics of the formation fluid may be detected at a range ofwavelengths, such as the near infrared (NIR) wavelength range ofapproximately 800-2500 nm, 1500-2050 nm, or 1600-1800 nm. Estimations offormation fluid properties according to one or more aspects of thepresent disclosure may utilize optical data collected at a singlewavelength, at multiple wavelengths, at a range of wavelengths, or atmultiple wavelength ranges.

The spectrometer 672 may measure one or more optical characteristics ofthe formation fluid flowing through the flowline 670 and output opticalspectra and/or other data representative of the detected opticalcharacteristics. The optical characteristics may include OD of theformation fluid at each of the detected wavelengths or wavelengthranges. The OD is a logarithmic measurement relating the intensity oflight emitted from the light source 692 to the intensity of lightdetected by the detector 694 at a certain wavelength or wavelengthrange. Each wavelength or range may correspond to a compositionalcomponent of the formation fluid. For example, each wavelength,wavelength range, or combination of wavelengths/ranges may pertain to acorresponding one of CO2, C1, C2, C3, C4, C5, and C6+, although otherarrangements are also within the scope of the present disclosure.

The spectrometer 672 may send optical spectra and/or other datarepresentative of the measured optical characteristics to a processor700 of the control/monitoring system 690. In the context of the presentdisclosure, the term “processor” refers to any number of processorcomponents. The processor 700 may include a single processor disposedonboard the downhole tool 650. In other implementations, at least aportion of the processor 700 (e.g., where multiple processorscollectively operate as the processor 700) may be located within thewellsite surface equipment 656 of FIG. 31, the logging and control unit544 of FIG. 30, and/or other surface equipment components. The processor700 may also or instead be or include one or more processors locatedwithin the downhole tool 650 and connected to one or more processorslocated in drilling and/or other equipment disposed at the wellsitesurface 516. Moreover, various combinations of processors may beconsidered part of the processor 700 in the following discussion.Similar terminology is applied with respect to the control/monitoringsystem 690, as well as a memory 702 of the control/monitoring system690, meaning that the control/monitoring system 690 may include variousprocessors communicatively coupled to each other and/or various memoriesat various locations.

The control/monitoring system 690 may estimate the FVF, GOR, and/orother parameters of the formation fluid, as described above, based onthe OD data received from the spectrometer 672, a density sensor, apressure sensor, a temperature sensor, and/or other sensors, and mayutilize the estimated FVF, GOR, and/or other parameters of the formationfluid to determine density, mass fractions of compositional components,OBM filtrate contamination, and/or other properties of the formationfluid. To make these and other determinations, the processor 700 mayexecute instructions stored in the memory 702.

The processor 700 may be communicatively coupled with one or moreoperator interfaces 706 and/or control devices 708. The operatorinterface 706 may include logs of predicted formation fluid propertiesthat are accessible to an operator. The control device 708 may includeone or more devices and/or portions thereof that receive control signalsfor operation based on the estimated properties of the formation fluid.Such control devices 708 may implement changes in depth of the downholetool 650 within the wellbore 526, adjustments to the pumping pressureand/or rate of the pump 668, and/or other control functions, perhapsbased on obtained, calculated, and/or estimated formation fluidproperties as described above.

One or more functions and/or other aspects of the downhole tool 650 mayalso be applicable or readily adaptable to at least a portion of thedownhole apparatus shown in FIG. 30. For example, one or more of the SWDtools, LWD modules 540, and/or MWD modules 542 shown in FIG. 30 and/ordescribed above may have one or more functions and/or other aspects incommon with a corresponding portion(s) of the downhole tool 650 shown inFIGS. 31 and 32.

FIG. 33 is a block diagram of an example processing system 800 that mayexecute example machine-readable instructions used to implement one ormore of the workflows, methods, and/or processes described herein,and/or to implement a portion of one or more of the example downholetools described herein.

The processing system 800 may be or comprise, for example, one or moreprocessors, controllers, special-purpose computing devices, servers,personal computers, personal digital assistant (PDA) devices,smartphones, internet appliances, and/or other types of computingdevices. Moreover, while it is possible that the entirety of the system800 shown in FIG. 33 is implemented within a downhole tool, such as thedownhole tools and/or modules shown in one or more of FIGS. 30-32, it isalso contemplated that one or more components or functions of the system800 may be implemented in wellsite surface equipment, perhaps includingthe logging and control unit 544 and/or other wellsite surface equipmentdepicted in FIG. 30 and/or the wellsite surface equipment 656 shown inFIG. 31.

The system 800 comprises a processor 812 such as, for example, ageneral-purpose programmable processor. The processor 812 includes alocal memory 814, and executes coded instructions 832 present in thelocal memory 814 and/or in another memory device. The processor 812 mayexecute, among other things, machine-readable instructions to implementthe methods and/or processes described herein. The processor 812 may be,comprise, or be implemented by various types of processing units, suchas one or more INTEL microprocessors, microcontrollers from the ARMand/or PICO families of microcontrollers, embedded soft/hard processorsin one or more FPGAs, etc. Of course, other processors from otherfamilies are also appropriate.

The processor 812 is in communication with a main memory including avolatile (e.g., random-access) memory 818 and a non-volatile (e.g.,read-only) memory 820 via a bus 822. The volatile memory 818 may be,comprise, or be implemented by static random access memory (SRAM),synchronous dynamic random access memory (SDRAM), dynamic random accessmemory (DRAM), RAMBUS dynamic random access memory (RDRAM) and/or othertypes of random access memory devices. The non-volatile memory 820 maybe, comprise, or be implemented by flash memory and/or other types ofmemory devices. One or more memory controllers (not shown) may controlaccess to the memory 818 and/or 820.

The processing system 800 also includes an interface circuit 824. Theinterface circuit 824 may be, comprise, or be implemented by varioustypes of standard interfaces, such as an Ethernet interface, a universalserial bus (USB), a third generation input/output (3GIO) interface, awireless interface, and/or a cellular interface, among others. Theinterface circuit 824 may also comprise a graphics driver card. Theinterface circuit 824 may also include a communication device such as amodem or network interface card to facilitate exchange of data withexternal computers via a network (e.g., Ethernet connection, digitalsubscriber line (DSL), telephone line, coaxial cable, cellular telephonesystem, satellite, etc.).

One or more input devices 826 are connected to the interface circuit824. The input device(s) 826 permit a user to enter data and commandsinto the processor 812. The input device(s) 826 may be, comprise, or beimplemented by, for example, a keyboard, a mouse, a touchscreen, atrack-pad, a trackball, an isopoint, and/or a voice recognition system,among others.

One or more output devices 828 are also connected to the interfacecircuit 824. The output devices 828 may be, comprise, or be implementedby, for example, display devices (e.g., a liquid crystal display orcathode ray tube display (CRT), among others), printers, and/orspeakers, among others.

The processing system 800 also includes one or more mass storage devices830 for storing machine-readable instructions and data. Examples of suchmass storage devices 830 include floppy disk drives, hard drive disks,compact disk drives, and digital versatile disk (DVD) drives, amongothers. The coded instructions 832 may be stored in the mass storagedevice 830, the volatile memory 818, the non-volatile memory 820, thelocal memory 814, and/or on a removable storage medium, such as a CD orDVD 834.

As an alternative to implementing the methods and/or apparatus describedherein in a system such as the processing system 800 of FIG. 33, methodsand or apparatus within the scope of the present disclosure may beembedded in another structure, such as a processor and/or anapplication-specific integrated circuit (ASIC).

In view of the entirety of the present disclosure, including the claimsand the figures, a person having ordinary skill in the art will readilyrecognize that the present disclosure introduces a method comprising:obtaining data associated with fluid obtained from a subterraneanformation, wherein the obtained fluid is obtained from the subterraneanformation via operation of a downhole sampling tool disposed proximatethe subterranean formation in a wellbore extending from a wellsitesurface into the subterranean formation, and wherein the obtained datais obtained via operation of at least one of the downhole sampling tooland surface equipment disposed at the wellsite surface and incommunication with the downhole sampling tool; and via operation of atleast one of the downhole sampling tool and the surface equipment:determining a start of linear behavior of a parameter in the obtaineddata, thus identifying linearly behaving data within the obtained data,wherein the linearly behaving data includes gas-oil ratio (GOR) data,density data, and optical density (OD) data; determining shrinkagefactor based on the linearly behaving data; obtaining a first functionrelating the GOR data with the determined shrinkage factor; obtaining asecond function relating the GOR data with the determined shrinkagefactor; determining a first linear relationship between the OD data andone of the first and second functions; determining a second linearrelationship between the density data and one of the first and secondfunctions; determining at least one first fluid property of oil-basedmud (OBM) filtrate contamination within the obtained fluid based on thefirst linear relationship; and determining at least one second fluidproperty of native formation fluid within the obtained fluid based onthe second linear relationship.

Determining at least one first fluid property of OBM filtratecontamination within the obtained fluid based on the first linearrelationship may comprise extrapolating the first linear relationship todetermine at least one first fluid property of OBM filtratecontamination within the obtained fluid.

Determining at least one second fluid property of native formation fluidwithin the obtained fluid based on the second linear relationship maycomprise extrapolating the second linear relationship to determine atleast one second fluid property of native formation fluid within theobtained fluid.

One of the first and second functions relating the GOR data with thedetermined shrinkage factor may be ƒ=[GOR₀−(GOR₀−GOR)b], where GOR₀ isGOR of the native formation fluid within the obtained fluid, GOR is GORof the obtained fluid, and b is the determined shrinkage factor.

One of the first and second functions relating the GOR data with thedetermined shrinkage factor may be g=(GOR₀−GOR)b, where GOR₀ is GOR ofthe native formation fluid within the obtained fluid, GOR is GOR of theobtained fluid, and b is the determined shrinkage factor.

The method may further comprise, via operation of at least one of thedownhole sampling tool and the surface equipment, denoising the obtaineddata prior to identifying the linearly behaving data.

Determining shrinkage factor based on the linearly behaving data maycomprise: determining formation volume factor (FVF) based on thelinearly behaving data; and determining shrinkage factor based on thedetermined FVF. Such method may further comprise, via operation of atleast one of the downhole sampling tool and the surface equipment,determining molecular weight of gas within the obtained fluid based onthe linearly behaving data, wherein determining the FVF may be furtherbased on the determined molecular weight of the gas. Such method mayalso or instead further comprise, via operation of at least one of thedownhole sampling tool and the surface equipment, determining a stocktank oil (STO) basis density of the obtained fluid at standardconditions based on the linearly behaving data, wherein determining theFVF may be further based on the determined STO-basis density. Suchmethod may also or instead further comprise, via operation of at leastone of the downhole sampling tool and the surface equipment, estimatingGOR of the native formation fluid based on the linearly behaving data,wherein determining the FVF may be further based on the estimated GOR.

The method may further comprise, via operation of at least one of thedownhole sampling tool and the surface equipment: power law fitting thedensity data to the GOR data; and comparing first end points of thepower law fitting with second end points of an extrapolation of thefirst or second functions.

The method may further comprise, via operation of at least one of thedownhole sampling tool and the surface equipment, estimating a volumefraction of the OBM filtrate contamination within the obtained fluidbased on the at least one first fluid property and the at least onesecond fluid property. Such method may further comprise, via operationof at least one of the downhole sampling tool and the surface equipment,converting the estimated volume fraction of the OBM filtratecontamination to an estimated weight fraction of the OBM filtratecontamination. Such method may further comprise, via operation of atleast one of the downhole sampling tool and the surface equipment,converting the estimated volume fraction of the OBM filtratecontamination to an estimated weight fraction of the OBM filtratecontamination on a stock tank oil (STO) basis. Such methods may furthercomprise, via operation of at least one of the downhole sampling tooland the surface equipment, determining a composition of the nativeformation fluid based on the estimated volume fraction of the OBMfiltrate contamination. Such methods may further comprise, via operationof at least one of the downhole sampling tool and the surface equipment,converting the estimated volume fraction of the OBM filtratecontamination from flowline conditions to formation conditions.

The estimated volume fraction of the OBM filtrate contamination withinthe obtained fluid may be a first estimated volume fraction, and themethod may further comprise, via operation of at least one of thedownhole sampling tool and the surface equipment: power law fitting thedensity data to the GOR data; determining a second estimated volumefraction of the OBM filtrate contamination within the obtained fluidbased on the power law fitting; and determining uncertainty associatedwith each of the first and second estimated volume fractions. Suchmethod may further comprise, via operation of at least one of thedownhole sampling tool and the surface equipment, averaging or selectingthe first and second estimated volume fractions based on the determineduncertainty.

The present disclosure also introduces a method comprising: obtainingdata associated with fluid obtained from a subterranean formation,wherein the obtained fluid is obtained from the subterranean formationvia operation of a downhole sampling tool disposed proximate thesubterranean formation in a wellbore extending from a wellsite surfaceinto the subterranean formation, wherein the obtained data is obtainedvia operation of at least one of the downhole sampling tool and surfaceequipment disposed at the wellsite surface and in communication with thedownhole sampling tool, and wherein the obtained data includes gas-oilratio (GOR) data; and via operation of at least one of the downholesampling tool and the surface equipment: estimating a stock tank oil(STO) basis density of the obtained fluid based on the obtained data;fitting the estimated STO-basis density of the obtained fluid as afunction of the GOR data to determine an STO-basis density of oil-basedmud (OBM) filtrate contamination in the obtained fluid and a parameterrelating the STO-basis density of the obtained fluid, the STO-basisdensity of the OBM filtrate contamination, and the GOR data; determiningthe STO-basis density of the obtained fluid based on the determinedSTO-basis density of the OBM filtrate contamination, the parameter, andthe GOR data, thus obtaining a log of the STO-basis density of theobtained fluid with respect to volume of the obtained fluid or timeelapsed during obtaining the obtained fluid; and determining an APIgravity log based on the log of the STO-basis density of the obtainedfluid.

The method may further comprise, via operation of at least one of thedownhole sampling tool and the surface equipment, denoising the obtaineddata prior to estimating the STO-basis density of the obtained fluid.

The method may further comprise, via operation of at least one of thedownhole sampling tool and the surface equipment: determining amolecular weight of gas within the obtained fluid based on the obtaineddata; estimating an initial formation volume factor (FVF) of theobtained fluid based on the log of the STO-basis density of the obtainedfluid and the determined molecular weight of gas within the obtainedfluid; fitting the initial FVF as a function of FVF of the OBM filtratecontamination, the GOR data, and a fitting parameter, thus obtaining alog of FVF of the obtained fluid with respect to volume of the obtainedfluid or time elapsed during obtaining the obtained fluid; determiningGOR of native formation fluid within the obtained fluid; and determininga final FVF of the obtained fluid based on the determined GOR of thenative fluid and the fitting parameter.

Determining the GOR of the native fluid may comprise: determiningshrinkage factor based on the determined final FVF of the obtainedfluid; obtaining a function relating the GOR data with the determinedshrinkage factor; determining a linear relationship between the functionand either the density data or the OD data; and determining the GOR ofthe native fluid based on the linear relationship. In suchimplementations, the function may be selected from the group consistingof: ƒ=[GOR₀−(GOR₀−GOR)b]; and g=(GOR₀−GOR)b; where GOR₀ is GOR of thenative formation fluid within the obtained fluid, GOR is GOR of theobtained fluid, and b is the determined shrinkage factor.

The present disclosure also introduces a method comprising: obtainingdata associated with fluid obtained from a subterranean formation,wherein the obtained fluid is obtained from the subterranean formationvia operation of a downhole sampling tool disposed proximate thesubterranean formation in a wellbore extending from a wellsite surfaceinto the subterranean formation, wherein the obtained data is obtainedvia operation of at least one of the downhole sampling tool and surfaceequipment disposed at the wellsite surface and in communication with thedownhole sampling tool, and wherein the obtained data includes gas-oilratio (GOR) data and formation volume factor (FVF) data; and viaoperation of at least one of the downhole sampling tool and the surfaceequipment: determining a linear relation between the FVF data and theGOR data; determining the slope of the linear relation; obtaining GORand density of the native formation fluid within the obtained fluid;determining stock tank oil (STO) basis density of native formation fluidwithin the obtained fluid based on the slope of the linear relation, GORof the native formation fluid, and density of the native formationfluid; determining a parameter relating STO-basis density of the nativeformation fluid, STO-basis density of oil-based mud (OBM) filtratecontamination within the obtained fluid, and GOR of the native formationfluid; and determining STO-basis density of the obtained fluid based onthe STO-basis density of OBM filtrate contamination, the parameter, andthe GOR data.

The method may further comprise, via operation of at least one of thedownhole sampling tool and the surface equipment, determining APIgravity of the obtained fluid based on the STO-basis density of theobtained fluid.

Obtaining GOR and density of the native formation fluid may comprise:determining a start of linear behavior of a parameter in the obtaineddata, thus identifying linearly behaving data within the obtained data;determining shrinkage factor based on the linearly behaving data;obtaining one or more functions relating the GOR data with thedetermined shrinkage factor; determining GOR of the native formationfluid based on at least one of the one or more functions; determining alinear relationship between the density data and one of the one or morefunctions; and determining density of the native formation fluid basedon the linear relationship. In such implementations, each of the one ormore functions may be selected from the group consisting of:ƒ=[GOR₀−(GOR₀−GOR)b]; and g=(GOR₀−GOR)b; where GOR₀ is GOR of the nativeformation fluid within the obtained fluid, GOR is GOR of the obtainedfluid, and b is the determined shrinkage factor.

The foregoing outlines features of several embodiments so that a personhaving ordinary skill in the art may better understand the aspects ofthe present disclosure. A person having ordinary skill in the art shouldappreciate that they may readily use the present disclosure as a basisfor designing or modifying other processes and structures for carryingout the same functions and/or achieving the same benefits of theembodiments introduced herein. A person having ordinary skill in the artshould also realize that such equivalent constructions do not departfrom the spirit and scope of the present disclosure, and that they maymake various changes, substitutions and alterations herein withoutdeparting from the spirit and scope of the present disclosure.

The Abstract at the end of this disclosure is provided to comply with 37C.F.R. § 1.72(b) to permit the reader to quickly ascertain the nature ofthe technical disclosure. It is submitted with the understanding that itwill not be used to interpret or limit the scope or meaning of theclaims.

What is claimed is:
 1. A method, comprising: estimating a stock tank oil(STO) basis density of a fluid obtained from a subterranean formationbased on data associated with the obtained fluid, wherein the dataincludes gas-oil ratio (GOR) data; fitting the estimated STO-basisdensity of the obtained fluid as a function of the GOR data to determinean STO-basis density of oil-based mud (OBM) filtrate contamination inthe obtained fluid and a parameter relating the STO-basis density of theobtained fluid, the STO-basis density of the OBM filtrate contamination,and the GOR data; determining the STO-basis density of the obtainedfluid based on the determined STO-basis density of the OBM filtratecontamination, the parameter, and the GOR data, thus obtaining a log ofthe STO-basis density of the obtained fluid with respect to volume ofthe obtained fluid or time elapsed during obtaining the obtained fluid;determining an API gravity log based on the log of the STO-basis densityof the obtained fluid; and estimating a volume fraction of OBM filtratecontamination within the obtained fluid based on the API gravity log. 2.The method of claim 1 comprising obtaining the data via operation of adownhole sampling tool disposed proximate the subterranean formation ina wellbore extending from a wellsite surface into the subterraneanformation, wherein the data is obtained via operation of the downholesampling tool, or surface equipment disposed at the wellsite surface incommunication with the downhole sampling tool, or both.
 3. The method ofclaim 1 comprising: determining a molecular weight of gas within theobtained fluid based on the obtained data; estimating an initialformation volume factor (FVF) of the obtained fluid based on the log ofthe STO-basis density of the obtained fluid and the determined molecularweight of gas within the obtained fluid; fitting the initial FVF as afunction of FVF of the OBM filtrate contamination, the GOR data, and afitting parameter, thus obtaining a log of FVF of the obtained fluidwith respect to volume of the obtained fluid or time elapsed duringobtaining the obtained fluid; determining GOR of native formation fluidwithin the obtained fluid; and determining a final FVF of the obtainedfluid based on the determined GOR of the native fluid and the fittingparameter.
 4. The method of claim 3 wherein determining the GOR of thenative formation fluid comprises: determining shrinkage factor based onthe determined final FVF of the obtained fluid; obtaining a functionrelating the GOR data with the determined shrinkage factor; determininga linear relationship between the function and either the density dataor the OD data; and determining the GOR of the native fluid based on thelinear relationship.
 5. A method, comprising: determining a linearrelation between formation volume factor (FVF) data of a fluid obtainedfrom a subterranean formation and gas-oil ratio (GOR) data of the fluid,wherein data associated with the obtained fluid includes the FVF dataand GOR data; determining stock tank oil (STO) basis density of nativeformation fluid within the obtained fluid based on a slope of the linearrelation, a GOR of the native formation fluid, and a density of thenative formation fluid; determining a parameter relating STO-basisdensity of the native formation fluid, STO-basis density of oil-basedmud (OBM) filtrate contamination within the obtained fluid, and GOR ofthe native formation fluid; and determining STO-basis density of theobtained fluid based on the STO-basis density of OBM filtratecontamination, the parameter, and the GOR data.
 6. The method of claim 5comprising obtaining the data via operation of a downhole sampling tooldisposed proximate the subterranean formation in a wellbore extendingfrom a wellsite surface into the subterranean formation, wherein thedata is obtained via operation of the downhole sampling tool, or surfaceequipment disposed at the wellsite surface in communication with thedownhole sampling tool, or both.
 7. The method of claim 5 comprisingdetermining API gravity of the obtained fluid based on the STO-basisdensity of the obtained fluid.